Translational and Rotational Control of an Asteroid Orbiting Satellite

The objective of this thesis is to analyze an effective control scheme for an asteroid orbiting satellite. The thesis first summarizes the progress made in the dynamics formulation of such satellites and then provides a theoretical framework for the control system design. The control objective is to maintain a nadir pointing attitude on a circular equatorial orbit. Using established control design techniques, feedback laws are constructed to control both rotational and translational motion of the satellite so that the control objective is achieved. Computer simulations are carried out to illustrate the effectiveness of the control laws

Tear-figures on Certain Minerals III

In the previous papers upon the same subject, the tear-fignres on stibnite, galena, spbalerite, pyrite, vivianite, enargite, calcite, gypsum, and barite were described. The characteristics of those on aragonite, alum and borax will be explained hereinafter

Tear-Figures on Certain Minerals II

In the first paper upon this subject, published in Octobar, 1916, the tear-figures on stibnite, galena, sphalerite, pyrite, vivianite and enargite were described; and in the following• the author desires to explain the characteristics of those on calcite, gypsum, and barite

Tear-figures on Certain Minerals IV

In the previous papers, the tear-figures on the minerals belonging to the regular, rhombic, hexagonal and monoclinic systems were discussed, in the following articles the author desires to explain the characteristics of those on crystals of the tetragonal and triclinic systems. For this purpose, the author has selected wulfenite and copper-sulphate crystals as the representatives of the minerals belonging to the two systems

Recursive gene selection based on maximum margin criterion: a comparison with SVM-RFE

BACKGROUND: In class prediction problems using microarray data, gene selection is essential to improve the prediction accuracy and to identify potential marker genes for a disease. Among numerous existing methods for gene selection, support vector machine-based recursive feature elimination (SVM-RFE) has become one of the leading methods and is being widely used. The SVM-based approach performs gene selection using the weight vector of the hyperplane constructed by the samples on the margin. However, the performance can be easily affected by noise and outliers, when it is applied to noisy, small sample size microarray data. RESULTS: In this paper, we propose a recursive gene selection method using the discriminant vector of the maximum margin criterion (MMC), which is a variant of classical linear discriminant analysis (LDA). To overcome the computational drawback of classical LDA and the problem of high dimensionality, we present efficient and stable algorithms for MMC-based RFE (MMC-RFE). The MMC-RFE algorithms naturally extend to multi-class cases. The performance of MMC-RFE was extensively compared with that of SVM-RFE using nine cancer microarray datasets, including four multi-class datasets. CONCLUSION: Our extensive comparison has demonstrated that for binary-class datasets MMC-RFE tends to show intermediate performance between hard-margin SVM-RFE and SVM-RFE with a properly chosen soft-margin parameter. Notably, MMC-RFE achieves significantly better performance with a smaller number of genes than SVM-RFE for multi-class datasets. The results suggest that MMC-RFE is less sensitive to noise and outliers due to the use of average margin, and thus may be useful for biomarker discovery from noisy data